Paper Info
Title
Evolutionary digital twin: A new approach for intelligent industrial product development
Abstract
To fulfill increasingly difficult and demanding tasks in the ever-changing complex world, intelligent industrial products are to be developed with higher flexibility and adaptability. Digital twin (DT) brings about a possible means, due to its ability to provide candidate behavior adjustments based on received “feedbacks” from its physical part. However, such candidate adjustments are deterministic, and thus lack of flexibility and adaptability. To address such problem, in this paper an extended concept – evolutionary digital twin (EDT) and an EDT-based new mode for intelligent industrial product development has been proposed. With our proposed EDT, a more precise approximated model of the physical world could be established through supervised learning, based on which the collaborative exploration for optimal policies via parallel simulation in multiple cyberspaces could be performed through reinforcement learning. Hence, more flexibility and adaptability could be brought to industrial products through machine learning (such as supervised learning and reinforcement learning) based self-evolution. As a primary verification of the effectiveness of our proposed approach, a case study has been carried out. The experimental results have well confirmed the effectiveness of our EDT based development mode.
Year
DOI
Venue
2021
10.1016/j.aei.2020.101209
Advanced Engineering Informatics
Keywords
DocType
Volume
Evolutionary digital twin,Intelligent industrial product,Collaborative evolution,Approximate world,Multiple cyber spaces,Simple evolution paradigm,Model evolution paradigm
Journal
47
ISSN
Citations 
PageRank 
1474-0346
1
0.37
References 
Authors
0
8
Name
Order
Citations
PageRank
Ting-Yu Lin1165.65
Zhengxuan Jia211.38
chen yang3236.31
Yingying Xiao412.73
Shulin Lan5429.39
Guoqiang Shi611.38
Bi Zeng710.37
Heyu Li810.37